Vehicle detection and identification through audio propagation

ABSTRACT

A method of motor vehicle detection and identification through audio propagation includes generating source autometric signatures; broadcasting autometric signatures; detecting the autometric signatures; visualizing the autometric signatures; determining known autometric signatures; and evaluating and sending autometric signatures to the motor vehicle.

INTRODUCTION

The present disclosure relates to vehicle detection and identification.More specifically, the present disclosure relates to vehicle detectionand identification through audio propagation.

As motor vehicles become more automated and the interface between a userand the vehicle increases, vehicles are increasingly expanding theirroles beyond passenger transportation. For example, an auditorysignaling mechanism emitted by the vehicle has the capability totransmit information during an emergency situation, as well as becomingan enabler of an interactive real-world gaming application.

While current vehicle identification systems achieve their intendedpurpose, there is a need for a new and improved system and method forvehicle detection and identification.

SUMMARY

According to several aspects, a method of motor vehicle detection andidentification through audio propagation includes generating sourceautometric signatures; broadcasting autometric signatures; detecting theautometric signatures; visualizing the autometric signatures;determining known autometric signatures; and evaluating and sendingautometric signatures to the motor vehicle.

In an additional aspect of the present disclosure, the method furtherincludes determining the source of the autometric signature data.

In another aspect of the present disclosure, the motor vehiclecommunicates with an information exchange infrastructure.

In another aspect of the present disclosure, the method further includesdetecting image similarities of the signatures with deep learning imagecomparison.

In another aspect of the present disclosure, the method further includeslistening for autometric signals with a mobile device.

In another aspect of the present disclosure, the mobile devicecommunicates with the information exchange infrastructure.

In another aspect of the present disclosure, generating autometricsignatures includes character to frequency mapping.

In another aspect of the present disclosure generating autometricsignatures includes character to temporal mapping.

In another aspect of the present disclosure, generating autometricsignatures includes character to color mapping.

In another aspect of the present disclosure, visualizing the autometricsignatures includes audio signature analysis and signal datavisualization.

In another aspect of the present disclosure, the method further includesstoring data associated with the autometric signatures in a sourcerepository.

According to several aspects, a method of motor vehicle detection andidentification through audio propagation includes generating sourceautometric signatures, including character to frequency mapping,character to temporal mapping, and character to color mapping;broadcasting autometric signatures and detecting the autometricsignatures with a device; visualizing the autometric signatures,including audio signature analysis and signal data visualization;determining known autometric signatures; and evaluating and sendingautometric signatures to the motor vehicle.

In another aspect of the present disclosure, the method further includesdetermining the source of the autometric signature data.

In another aspect of the present disclosure, the motor vehiclecommunicates with an information exchange infrastructure.

In another aspect of the present disclosure, the method further includesdetecting image similarities of the signatures with deep learning imagecomparison.

In another aspect of the present disclosure, the method further includeslistening for autometric signals with a mobile device.

In another aspect of the present disclosure, the mobile devicecommunicates with the information exchange infrastructure.

In another aspect of the present disclosure, the method further includesstoring data associated with the autometric signatures in a sourcerepository.

According to several aspects, a method of motor vehicle detection andidentification through audio propagation includes generating sourceautometric signatures, including character to frequency mapping,character to temporal mapping, and character to color mapping;visualizing the autometric signatures, including audio signatureanalysis and signal data visualization; determining known autometricsignatures; detecting image similarities of the signatures with deeplearning image comparison; and sending autometric signature to the motorvehicle. The motor vehicle communicates with an information exchangeinfrastructure and a mobile device listens to the autometric signals andcommunicates with the information exchange infrastructure.

In another aspect of the present disclosure, the method furthercomprises evaluating a sequence of chords to determine a distinct audiosignature.

Further areas of applicability will become apparent from the descriptionprovided herein. It should be understood that the description andspecific examples are intended for purposes of illustration only and arenot intended to limit the scope of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are for illustration purposes only and arenot intended to limit the scope of the present disclosure in any way.

FIG. 1 is a block diagram of a system for vehicle detection andidentification according to an exemplary embodiment;

FIG. 2 is an image of an autometrics viability test with the systemshown in FIG. 1 according to an exemplary embodiment;

FIG. 3 is a drawing of data encoded frequency audio tones produced withthe system shown in FIG. 1 according to an exemplary embodiment; and

FIGS. 4 and 5 are drawings of data encoded rhythm audio tones producedwith the system shown in FIG. 1 according to an exemplary embodiment.

DETAILED DESCRIPTION

The following description is merely exemplary in nature and is notintended to limit the present disclosure, application, or uses.

Referring to FIG. 1 , there is shown a system 10 that enables a vehicle12 to transmit a uniquely identifiable audio signal 16 to communicatedata as a gamification enabler or in situations where the vehicle 12 isnot easily within reach. A detection apparatus, such as, for example, amobile device 18 leverages the microphone capability of the mobiledevice 18 to listen and communicate with a back-office.

The back-office 20 provides an analytic process for audio autometrics.The back-office 20 includes a source autometrics signature generationmodule 28, a data visualization module 26, a storage module 36, an imagesimilarity detection module 24, and an evaluation and send module 22.The mobile device 18 communicates directly with the back-office 20, aswell as with the vehicle 12 and the back-office 20 through aninformation exchange infrastructure 14.

The autometrics signature generation module 28 includes a set ofsub-modules 50, 52 and 54. The sub-module 50 encodes source dataemploying algorithmic mapping of audio frequencies, character andcharacter position; the sub-module 52 determines the simultaneous tonetiming and duration; and the sub-module 54 encodes an audio file andstores it in a known autometrics signature container. Information fromthe autometrics signature generation module 28 is transmitted to thedata visualization module 26 and the storage module 36.

The data visualization module 26 includes a set of sub-modules 40, 42and 46. The sub-module 40 filters, analyzes and represents autometricssignal data that is biased for frequency and biased for temporalsignatures; the sub-module 42 generates vehicle audio signaturedepiction of the autometrics as sample data 44; and with the sample data44, the sub-module 46 produces a colorized image 48 by removing unusedfrequencies and mapping the leveraged frequencies to a predefined RGBcolor map. FIG. 2 illustrates the amplitude and duration of thedetection of 16 separate tones. Further details of the analytic processassociated with the back-office 20 are described below.

The system 10 encodes and stores vehicle, gamer and exclusive attributes(metadata) leveraging a combination of audio and color to create avehicle/object specific audio signature message and correspondingencoded message image representation. The system 10 creates theequivalent of a musical chord (simultaneously played tones) for eachpiece of data utilizing a selected frequency range, as shown in FIG. 3 .Each chord provides an object ID of a vehicle, the type of the vehicle,and the trim level of the vehicle.

The execution of chords in sequence is enhanced by purposeful pauses(silences) between chord broadcasts. These pauses are applied in achord-silence alternating sequence. This sequence establishes a rhythm(chord and silence durations, as shown in FIG. 4 ). Rhythm is appliedutilizing a mathematic function. For example, given an initial durationminimum value of x (in milliseconds), a duration separation value of y(in milliseconds), and a character position z (position of the characterin the character set), the expression d (duration) = x + (y * z) - y.The unique (vehicle or object specific) sequence of chords and silencesis also represented as a synesthetic-like image depiction that conveysthe frequency and temporal aspects of the encoded message. Hence, theautometric is the unique combination of frequency chords and associatedrhythm utilized to convey data.

Referring back to FIG. 1 , the source autometric signature generationmodule 28 provides character to frequency mapping, and character totemporal mapping. For character to frequency mapping, the first and lastchords in each transmission are pre-defined sets of sync tones outsidethe range of the character mapping. Content strings are encoded intochords, giving each a distinct audio signature within a definedcharacter set. Each character of a content string is represented by afrequency value (Hz) employing the following expression:Hz=(hz_minimum+((position_in_string*character set length*hz_delta)+((character number+1)*hz_delta))-hz_delta).

For character to temporal mapping, the duration of each chord and theintervening silences are derived with the expression:ms=ms_minimum+(position*ms_delta) - ms_delta. And for character to colormapping, a hue, saturation, and luminosity (HSL) color value is assignedto each character based on the character and its position in its parentcontent string. Data 38 from the source autometrics signature generationmodule 28 is transmitted to the data visualization module 26. Data fromthe source autometric signature generation module 28 is also transmittedto the storage module 36 as known autometric signatures.

The data visualization module 26 encodes the data 38. Specifically, thedata visualization module 26 provides audio signature analysis andsignal visualization, starting with an audio file 38. The signal datavisualization provides a spectrogram image, which is the data processedto generate black and white spectrogram image representing the completetime domain and frequency domain, and colorized image depiction, whichis data that is processed to generate a compressed, colorized image.

Data from the data visualization module 26 is transmitted to a decisionmodule 34, which determines the source of the audio data. If the sourceis from the autometrics signature generation module 28, the data fromthe data visualization module is transmitted to the storage module 36.Vehicle signature messages, vehicle attributes, vehicle black and whitespectrogram and vehicle colorized spectrogram are sent to a distributeddata mining repository. Image files and audio files are distributed(shared) across the data mining repository with capability to increasethe footprint to reduce data density per physical resource as datavolume increases.

Next the audio file is sent to the evaluation and send module 22. Here,audio signature messages are sent as over-the-air (OTA) file push to thevehicle audio internal module for broadcast. Further, sound broadcastconditions are enabled via vehicle internal module triggers. Forexample, business use cases may require interaction from the vehicleoperator to be enabled. As such, the vehicle 12 plays the audiosignature message leveraging the infotainment or external speakersystems.

Next, the mobile device 18 provides detection and interaction with thecloud. Through the mobile device 18, indicators are sent to the user toconfirm that the microphone is active and is recording and to initiateaudio recording. Feedback to the user is provided for both successfuldetection and failed engagements (via SMS/MMS/Push) by a two-factorinteraction model.

Further, the mobile device 18 sends information OTA to the back office20, in particular, the data visualization module 26. In variousarrangements, this information can contain encapsulated applicablemetadata and audio files, including, such as, for example, gamificationsending account, gamification destination account, emergency sendingaccount, and emergency destination account.

If the decision module 34 determines that the source of the data fromthe data visualization module 26 originated from the mobile device 18,the data is transmitted to the image similarity detection module 24 withdeep learning image comparison of a set of images 30 to execute ananalytic match 32.

The module 24 analyzes RGB content of receiver compressed colorizedimages to identify the sync chord and end chord to align the messagestart and end points. The module 24 also makes a pixel-by-pixelcomparison against the compressed colorized images in the storage module36. The output of the module 24 is a pixel match distributionpercentage, and the match success is based on a tolerance criteria.

As stated above, the module 24 also provides high level Al deeplearning. Accordingly, the data set and known outcomes are utilized togenerate a decision model that is applied to fresh data to makejudgements. Dependent applications also utilize the model to increaseexecution confidence. When new data is available, the model isre-trained and improved and the model is applied to fresh data to makejudgements. In addition, the module 24 performs a population comparisonsize reduction utilizing image analysis. Hence, feature extraction isutilized to qualify an image as a member of a sub-population for deeplearning comparisons. Sub-population approximations are performed by thedeep learning system to identify confidence math level tolerances toaddress potential uncertainty derived from the broadcast attributes.Information is transmitted (SMS/MMS/Push) back to the mobile device 18that an image match is found. Information may also be transmitted toother business systems, such as, for example, web service API.

The system 10 provides one or more of the following benefits: enablesgaming activities played in an extensive outdoor area involving thecollection of vehicle broadcasts to receive interactive feedback poweredby a mobile application detection mechanism; provide communication of avehicle’s metadata; provides an available detection apparatus with anapplication that leverages the microphone capability to listen and sendcaptured audio and receive decoded signature messages and metadata; andprovides an approach to detect broadcast metadata by law enforcementvehicles following a suspect vehicle within a physical distance range oremergency first responders identifying vehicles in distress.

The description of the present disclosure is merely exemplary in natureand variations that do not depart from the gist of the presentdisclosure are intended to be within the scope of the presentdisclosure. Such variations are not to be regarded as a departure fromthe spirit and scope of the present disclosure.

What is claimed is:
 1. A method of motor vehicle detection andidentification through audio propagation, the method comprising:generating source autometric signatures; broadcasting the autometricsignatures; detecting the autometric signatures; visualizing theautometric signatures; determining known autometric signatures; andevaluating and sending autometric signatures to the motor vehicle. 2.The method of claim 1 further comprising determining a source of theautometric signature data.
 3. The method of claim 1, wherein the motorvehicle communicates with an information exchange infrastructure.
 4. Themethod of claim 1 further comprising detecting image similarities of thesignatures with deep learning image comparison.
 5. The method of claim 1further comprising listening for autometric signals with a mobiledevice.
 6. The method of claim 5, wherein the mobile device communicateswith the information exchange infrastructure.
 7. The method of claim 1,wherein generating autometric signatures includes character to frequencymapping.
 8. The method of claim 1, wherein generating autometricsignatures includes character to temporal mapping.
 9. The method ofclaim 1, wherein generating autometric signatures includes character tocolor mapping.
 10. The method of claim 1, wherein visualizing theautometric signatures includes audio signature analysis and signal datavisualization.
 11. The method of claim 1 further comprising storing dataassociated with the autometric signatures in a source repository.
 12. Amethod of motor vehicle detection and identification through audiopropagation, the method comprising: generating source autometricsignatures, including character to frequency mapping, character totemporal mapping, and character to color mapping; broadcastingautometric signatures and detecting the autometric signatures with adevice; visualizing the autometric signatures, including audio signatureanalysis and signal data visualization; determining known autometricsignatures; and evaluating and sending autometric signatures to themotor vehicle.
 13. The method of claim 12 further comprising determiningthe source of the autometric signature data.
 14. The method of claim 12,wherein the motor vehicle communicates with an information exchangeinfrastructure.
 15. The method of claim 14 further comprising detectingimage similarities of the signatures with deep learning imagecomparison.
 16. The method of claim 14 further comprising listening forautometric signals with a mobile device.
 17. The method of claim 16,wherein the mobile device communicates with the information exchangeinfrastructure.
 18. The method of claim 12 further comprising storingdata associated with the autometric signatures in a source repository.19. A method of motor vehicle detection and identification through audiopropagation, the method comprising: generating source autometricsignatures, including character to frequency mapping, character totemporal mapping, and character to color mapping; visualizing theautometric signatures, including audio signature analysis and signaldata visualization; visualizing the autometric signatures; determiningknown autometric signatures; detecting image similarities of thesignatures with deep learning image comparison; and sending theautometric signatures to the motor vehicle, wherein the motor vehiclecommunicates with an information exchange infrastructure and a mobiledevice listens to the autometric signals and communicates with theinformation exchange infrastructure.
 20. The method of claim 19, furthercomprising evaluating a sequence of chords to determine a distinct audiosignature.